We design and implement a music-tune analysis system to realize automatic emotion identification and prediction based on acoustic signal data. To compute physical elements of music pieces we define three significant tunes parameters. These are: repeated parts or repetitions inside a tune, thumbnail of a music piece, and homogeneity pattern of a tune. They are significant, because they are related to how people perceive music pieces. By means of these three parameters we can express the essential features of emotional-aspects of each piece. Our system consists of music-tune features database and computational mechanism for comparison between different tunes. Based on Hevner's emotions adjectives groups we created a new way of emotion presentation on emotion's plane with two axes: activity and happiness. That makes it possible to determine perceived emotions of listening to a tune and calculate adjacent emotions on a plane. Finally, we performed a set of experiments on western classical and popular music pieces, which presented that our proposed approach reached 72% precision ratio and show a positive trend of system's efficiency when database size is increasing.